Smart Home IoT Anomaly Detection based on Ensemble Model Learning From Heterogeneous Data

Nowadays, internet based home automation is made possible with the advent of intelligent device control. These electronic sensing devices transfer an enormous amount of data into the cloud. It is a challenge to discover hidden information from the massive amount of stored data in the cloud. In addition, privacy, security, and stability could also be a concern for users. Due to these issues becoming ever more prevalent in today’s society, the need to have access to readily anomaly detection becomes crucial for the modern smart home user. In this paper, we design, test and evaluate an ensemble model anomaly detection method. Our method targets the data anomalies present in general smart Internet of Things (IoT) devices, allowing for easy detection of anomalous events based on stored data. We make our method robust through ensemble machine learning model training. We aim to simulate different types of anomaly situations on publicly available smart home data sets, thereby exposing our models to likely real world phenomenons and events that may cause anomalies. Experiments are conducted on the processed data and evaluated for accuracy through validation and testing against independent and identically distributed labeled data.